top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2004
Descrizione fisica 1 online resource (366 p.)
Disciplina 572.8636
572.865
Altri autori (Persone) DoKim-Anh <1960->
AmbroiseChristophe <1969->
Collana Wiley series in probability and statistics
Soggetto topico DNA microarrays - Statistical methods
Gene expression - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 1-280-25332-0
9786610253326
0-470-35030-X
0-471-72612-5
0-471-72842-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays
2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space
3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions
3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic
3.17.2 The Clest Method for the Number of Clusters
Record Nr. UNINA-9910146082203321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2004
Descrizione fisica 1 online resource (366 p.)
Disciplina 572.8636
572.865
Altri autori (Persone) DoKim-Anh <1960->
AmbroiseChristophe <1969->
Collana Wiley series in probability and statistics
Soggetto topico DNA microarrays - Statistical methods
Gene expression - Statistical methods
ISBN 1-280-25332-0
9786610253326
0-470-35030-X
0-471-72612-5
0-471-72842-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays
2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space
3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions
3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic
3.17.2 The Clest Method for the Number of Clusters
Record Nr. UNINA-9910830637803321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyzing microarray gene expression data / / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Analyzing microarray gene expression data / / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2004
Descrizione fisica 1 online resource (366 p.)
Disciplina 572.8/636
Altri autori (Persone) DoKim-Anh <1960->
AmbroiseChristophe <1969->
Collana Wiley series in probability and statistics
Soggetto topico DNA microarrays - Statistical methods
Gene expression - Statistical methods
ISBN 9786610253326
9781280253324
1280253320
9780470350300
047035030X
9780471726128
0471726125
9780471728429
047172842X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays
2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space
3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions
3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic
3.17.2 The Clest Method for the Number of Clusters
Record Nr. UNINA-9911019928803321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan
The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan
Autore McLachlan Geoffrey J. <1946->
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2008
Descrizione fisica 1 online resource (399 p.)
Disciplina 519.5
519.5/44
519.544
Altri autori (Persone) KrishnanT <1938-> (Thriyambakam)
Collana Wiley series in probability and statistics
Soggetto topico Expectation-maximization algorithms
Estimation theory
Missing observations (Statistics)
ISBN 1-281-28447-5
9786611284473
0-470-19161-9
0-470-19160-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto The EM Algorithm and Extensions; CONTENTS; PREFACE TO THE SECOND EDITION; PREFACE TO THE FIRST EDITION; LIST OF EXAMPLES; 1 GENERAL INTRODUCTION; 1.1 Introduction; 1.2 Maximum Likelihood Estimation; 1.3 Newton-Type Methods; 1.3.1 Introduction; 1.3.2 Newton-Raphson Method; 1.3.3 Quasi-Newton Methods; 1.3.4 Modified Newton Methods; 1.4 Introductory Examples; 1.4.1 Introduction; 1.4.2 Example 1.1: A Multinomial Example; 1.4.3 Example 1.2: Estimation of Mixing Proportions; 1.5 Formulation of the EM Algorithm; 1.5.1 EM Algorithm; 1.5.2 Example 1.3: Censored Exponentially Distributed Survival Times
1.5.3 E- and M-Steps for the Regular Exponential Family1.5.4 Example 1.4: Censored Exponentially Distributed Survival Times (Example 1.3 Continued); 1.5.5 Generalized EM Algorithm; 1.5.6 GEM Algorithm Based on One Newton-Raphson Step; 1.5.7 EM Gradient Algorithm; 1.5.8 EM Mapping; 1.6 EM Algorithm for MAP and MPL Estimation; 1.6.1 Maximum a Posteriori Estimation; 1.6.2 Example 1.5: A Multinomial Example (Example 1.1 Continued); 1.6.3 Maximum Penalized Estimation; 1.7 Brief Summary of the Properties of the EM Algorithm; 1.8 History of the EM Algorithm; 1.8.1 Early EM History
1.8.2 Work Before Dempster, Laird, and Rubin (1977)1.8.3 EM Examples and Applications Since Dempster, Laird, and Rubin (1977); 1.8.4 Two Interpretations of EM; 1.8.5 Developments in EM Theory, Methodology, and Applications; 1.9 Overview of the Book; 1.10 Notations; 2 EXAMPLES OF THE EM ALGORITHM; 2.1 Introduction; 2.2 Multivariate Data with Missing Values; 2.2.1 Example 2.1: Bivariate Normal Data with Missing Values; 2.2.2 Numerical Illustration; 2.2.3 Multivariate Data: Buck's Method; 2.3 Least Squares with Missing Data; 2.3.1 Healy-Westmacott Procedure
2.3.2 Example 2.2: Linear Regression with Missing Dependent Values2.3.3 Example 2.3: Missing Values in a Latin Square Design; 2.3.4 Healy-Westmacott Procedure as an EM Algorithm; 2.4 Example 2.4: Multinomial with Complex Cell Structure; 2.5 Example 2.5: Analysis of PET and SPECT Data; 2.6 Example 2.6: Multivariate t-Distribution (Known D.F.); 2.6.1 ML Estimation of Multivariate t-Distribution; 2.6.2 Numerical Example: Stack Loss Data; 2.7 Finite Normal Mixtures; 2.7.1 Example 2.7: Univariate Component Densities; 2.7.2 Example 2.8: Multivariate Component Densities
2.7.3 Numerical Example: Red Blood Cell Volume Data2.8 Example 2.9: Grouped and Truncated Data; 2.8.1 Introduction; 2.8.2 Specification of Complete Data; 2.8.3 E-Step; 2.8.4 M-Step; 2.8.5 Confirmation of Incomplete-Data Score Statistic; 2.8.6 M-Step for Grouped Normal Data; 2.8.7 Numerical Example: Grouped Log Normal Data; 2.9 Example 2.10: A Hidden Markov AR(1) model; 3 BASIC THEORY OF THE EM ALGORITHM; 3.1 Introduction; 3.2 Monotonicity of the EM Algorithm; 3.3 Monotonicity of a Generalized EM Algorithm; 3.4 Convergence of an EM Sequence to a Stationary Value; 3.4.1 Introduction
3.4.2 Regularity Conditions of Wu (1983)
Record Nr. UNINA-9910145008603321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan
The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan
Autore McLachlan Geoffrey J. <1946->
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2008
Descrizione fisica 1 online resource (399 p.)
Disciplina 519.5
519.5/44
519.544
Altri autori (Persone) KrishnanT <1938-> (Thriyambakam)
Collana Wiley series in probability and statistics
Soggetto topico Expectation-maximization algorithms
Estimation theory
Missing observations (Statistics)
ISBN 1-281-28447-5
9786611284473
0-470-19161-9
0-470-19160-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto The EM Algorithm and Extensions; CONTENTS; PREFACE TO THE SECOND EDITION; PREFACE TO THE FIRST EDITION; LIST OF EXAMPLES; 1 GENERAL INTRODUCTION; 1.1 Introduction; 1.2 Maximum Likelihood Estimation; 1.3 Newton-Type Methods; 1.3.1 Introduction; 1.3.2 Newton-Raphson Method; 1.3.3 Quasi-Newton Methods; 1.3.4 Modified Newton Methods; 1.4 Introductory Examples; 1.4.1 Introduction; 1.4.2 Example 1.1: A Multinomial Example; 1.4.3 Example 1.2: Estimation of Mixing Proportions; 1.5 Formulation of the EM Algorithm; 1.5.1 EM Algorithm; 1.5.2 Example 1.3: Censored Exponentially Distributed Survival Times
1.5.3 E- and M-Steps for the Regular Exponential Family1.5.4 Example 1.4: Censored Exponentially Distributed Survival Times (Example 1.3 Continued); 1.5.5 Generalized EM Algorithm; 1.5.6 GEM Algorithm Based on One Newton-Raphson Step; 1.5.7 EM Gradient Algorithm; 1.5.8 EM Mapping; 1.6 EM Algorithm for MAP and MPL Estimation; 1.6.1 Maximum a Posteriori Estimation; 1.6.2 Example 1.5: A Multinomial Example (Example 1.1 Continued); 1.6.3 Maximum Penalized Estimation; 1.7 Brief Summary of the Properties of the EM Algorithm; 1.8 History of the EM Algorithm; 1.8.1 Early EM History
1.8.2 Work Before Dempster, Laird, and Rubin (1977)1.8.3 EM Examples and Applications Since Dempster, Laird, and Rubin (1977); 1.8.4 Two Interpretations of EM; 1.8.5 Developments in EM Theory, Methodology, and Applications; 1.9 Overview of the Book; 1.10 Notations; 2 EXAMPLES OF THE EM ALGORITHM; 2.1 Introduction; 2.2 Multivariate Data with Missing Values; 2.2.1 Example 2.1: Bivariate Normal Data with Missing Values; 2.2.2 Numerical Illustration; 2.2.3 Multivariate Data: Buck's Method; 2.3 Least Squares with Missing Data; 2.3.1 Healy-Westmacott Procedure
2.3.2 Example 2.2: Linear Regression with Missing Dependent Values2.3.3 Example 2.3: Missing Values in a Latin Square Design; 2.3.4 Healy-Westmacott Procedure as an EM Algorithm; 2.4 Example 2.4: Multinomial with Complex Cell Structure; 2.5 Example 2.5: Analysis of PET and SPECT Data; 2.6 Example 2.6: Multivariate t-Distribution (Known D.F.); 2.6.1 ML Estimation of Multivariate t-Distribution; 2.6.2 Numerical Example: Stack Loss Data; 2.7 Finite Normal Mixtures; 2.7.1 Example 2.7: Univariate Component Densities; 2.7.2 Example 2.8: Multivariate Component Densities
2.7.3 Numerical Example: Red Blood Cell Volume Data2.8 Example 2.9: Grouped and Truncated Data; 2.8.1 Introduction; 2.8.2 Specification of Complete Data; 2.8.3 E-Step; 2.8.4 M-Step; 2.8.5 Confirmation of Incomplete-Data Score Statistic; 2.8.6 M-Step for Grouped Normal Data; 2.8.7 Numerical Example: Grouped Log Normal Data; 2.9 Example 2.10: A Hidden Markov AR(1) model; 3 BASIC THEORY OF THE EM ALGORITHM; 3.1 Introduction; 3.2 Monotonicity of the EM Algorithm; 3.3 Monotonicity of a Generalized EM Algorithm; 3.4 Convergence of an EM Sequence to a Stationary Value; 3.4.1 Introduction
3.4.2 Regularity Conditions of Wu (1983)
Record Nr. UNINA-9910831039703321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The EM algorithm and extensions / / Geoffrey J. McLachlan, Thriyambakam Krishnan
The EM algorithm and extensions / / Geoffrey J. McLachlan, Thriyambakam Krishnan
Autore McLachlan Geoffrey J. <1946->
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2008
Descrizione fisica 1 online resource (399 p.)
Disciplina 519.5/44
Altri autori (Persone) KrishnanT <1938-> (Thriyambakam)
Collana Wiley series in probability and statistics
Soggetto topico Expectation-maximization algorithms
Estimation theory
Missing observations (Statistics)
ISBN 9786611284473
9781281284471
1281284475
9780470191613
0470191619
9780470191606
0470191600
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto The EM Algorithm and Extensions; CONTENTS; PREFACE TO THE SECOND EDITION; PREFACE TO THE FIRST EDITION; LIST OF EXAMPLES; 1 GENERAL INTRODUCTION; 1.1 Introduction; 1.2 Maximum Likelihood Estimation; 1.3 Newton-Type Methods; 1.3.1 Introduction; 1.3.2 Newton-Raphson Method; 1.3.3 Quasi-Newton Methods; 1.3.4 Modified Newton Methods; 1.4 Introductory Examples; 1.4.1 Introduction; 1.4.2 Example 1.1: A Multinomial Example; 1.4.3 Example 1.2: Estimation of Mixing Proportions; 1.5 Formulation of the EM Algorithm; 1.5.1 EM Algorithm; 1.5.2 Example 1.3: Censored Exponentially Distributed Survival Times
1.5.3 E- and M-Steps for the Regular Exponential Family1.5.4 Example 1.4: Censored Exponentially Distributed Survival Times (Example 1.3 Continued); 1.5.5 Generalized EM Algorithm; 1.5.6 GEM Algorithm Based on One Newton-Raphson Step; 1.5.7 EM Gradient Algorithm; 1.5.8 EM Mapping; 1.6 EM Algorithm for MAP and MPL Estimation; 1.6.1 Maximum a Posteriori Estimation; 1.6.2 Example 1.5: A Multinomial Example (Example 1.1 Continued); 1.6.3 Maximum Penalized Estimation; 1.7 Brief Summary of the Properties of the EM Algorithm; 1.8 History of the EM Algorithm; 1.8.1 Early EM History
1.8.2 Work Before Dempster, Laird, and Rubin (1977)1.8.3 EM Examples and Applications Since Dempster, Laird, and Rubin (1977); 1.8.4 Two Interpretations of EM; 1.8.5 Developments in EM Theory, Methodology, and Applications; 1.9 Overview of the Book; 1.10 Notations; 2 EXAMPLES OF THE EM ALGORITHM; 2.1 Introduction; 2.2 Multivariate Data with Missing Values; 2.2.1 Example 2.1: Bivariate Normal Data with Missing Values; 2.2.2 Numerical Illustration; 2.2.3 Multivariate Data: Buck's Method; 2.3 Least Squares with Missing Data; 2.3.1 Healy-Westmacott Procedure
2.3.2 Example 2.2: Linear Regression with Missing Dependent Values2.3.3 Example 2.3: Missing Values in a Latin Square Design; 2.3.4 Healy-Westmacott Procedure as an EM Algorithm; 2.4 Example 2.4: Multinomial with Complex Cell Structure; 2.5 Example 2.5: Analysis of PET and SPECT Data; 2.6 Example 2.6: Multivariate t-Distribution (Known D.F.); 2.6.1 ML Estimation of Multivariate t-Distribution; 2.6.2 Numerical Example: Stack Loss Data; 2.7 Finite Normal Mixtures; 2.7.1 Example 2.7: Univariate Component Densities; 2.7.2 Example 2.8: Multivariate Component Densities
2.7.3 Numerical Example: Red Blood Cell Volume Data2.8 Example 2.9: Grouped and Truncated Data; 2.8.1 Introduction; 2.8.2 Specification of Complete Data; 2.8.3 E-Step; 2.8.4 M-Step; 2.8.5 Confirmation of Incomplete-Data Score Statistic; 2.8.6 M-Step for Grouped Normal Data; 2.8.7 Numerical Example: Grouped Log Normal Data; 2.9 Example 2.10: A Hidden Markov AR(1) model; 3 BASIC THEORY OF THE EM ALGORITHM; 3.1 Introduction; 3.2 Monotonicity of the EM Algorithm; 3.3 Monotonicity of a Generalized EM Algorithm; 3.4 Convergence of an EM Sequence to a Stationary Value; 3.4.1 Introduction
3.4.2 Regularity Conditions of Wu (1983)
Record Nr. UNINA-9911020436103321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Finite mixture models [[electronic resource] /] / Geoffrey McLachlan, David Peel
Finite mixture models [[electronic resource] /] / Geoffrey McLachlan, David Peel
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa New York, : Wiley, c2000
Descrizione fisica 1 online resource (450 p.)
Disciplina 519
519.2
519.532
Altri autori (Persone) PeelDavid <1971->
Collana Wiley series in probability and statistics. Applied probability and statistics section
Soggetto topico Mixture distributions (Probability theory)
Mathematics
ISBN 1-280-26492-6
9786610264926
0-470-34190-4
0-471-65406-X
0-471-72118-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; Preface; 1 General Introduction; 2 ML Fitting of Mixture Models; 3 Multivariate Normal Mixtures; 4 Bayesian Approach to Mixture Analysis; 5 Mixtures with Nonnormal Components; 6 Assessing the Number of Components in Mixture Models; 7 Multivariate t Mixtures; 8 Mixtures of Factor Analyzers; 9 Fitting Mixture Models to Binned Data; 10 Mixture Models for Failure-Time Data; 11 Mixture Analysis of Directional Data; 12 Variants of the EM Algorithm for Large Databases; 13 Hidden Markov Models; Appendix: Mixture Software; References; Author Index; Subject Index
Record Nr. UNINA-9910143199603321
McLachlan Geoffrey J. <1946->  
New York, : Wiley, c2000
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Finite mixture models [[electronic resource] /] / Geoffrey McLachlan, David Peel
Finite mixture models [[electronic resource] /] / Geoffrey McLachlan, David Peel
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa New York, : Wiley, c2000
Descrizione fisica 1 online resource (450 p.)
Disciplina 519
519.2
519.532
Altri autori (Persone) PeelDavid <1971->
Collana Wiley series in probability and statistics. Applied probability and statistics section
Soggetto topico Mixture distributions (Probability theory)
Mathematics
ISBN 1-280-26492-6
9786610264926
0-470-34190-4
0-471-65406-X
0-471-72118-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; Preface; 1 General Introduction; 2 ML Fitting of Mixture Models; 3 Multivariate Normal Mixtures; 4 Bayesian Approach to Mixture Analysis; 5 Mixtures with Nonnormal Components; 6 Assessing the Number of Components in Mixture Models; 7 Multivariate t Mixtures; 8 Mixtures of Factor Analyzers; 9 Fitting Mixture Models to Binned Data; 10 Mixture Models for Failure-Time Data; 11 Mixture Analysis of Directional Data; 12 Variants of the EM Algorithm for Large Databases; 13 Hidden Markov Models; Appendix: Mixture Software; References; Author Index; Subject Index
Record Nr. UNINA-9910830949803321
McLachlan Geoffrey J. <1946->  
New York, : Wiley, c2000
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Finite mixture models / / Geoffrey McLachlan, David Peel
Finite mixture models / / Geoffrey McLachlan, David Peel
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa New York, : Wiley, c2000
Descrizione fisica 1 online resource (450 p.)
Disciplina 519.2
Altri autori (Persone) PeelDavid <1971->
Collana Wiley series in probability and statistics. Applied probability and statistics section
Soggetto topico Mixture distributions (Probability theory)
Mathematics
ISBN 9786610264926
9781280264924
1280264926
9780470341902
0470341904
9780471654063
047165406X
9780471721185
0471721182
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; Preface; 1 General Introduction; 2 ML Fitting of Mixture Models; 3 Multivariate Normal Mixtures; 4 Bayesian Approach to Mixture Analysis; 5 Mixtures with Nonnormal Components; 6 Assessing the Number of Components in Mixture Models; 7 Multivariate t Mixtures; 8 Mixtures of Factor Analyzers; 9 Fitting Mixture Models to Binned Data; 10 Mixture Models for Failure-Time Data; 11 Mixture Analysis of Directional Data; 12 Variants of the EM Algorithm for Large Databases; 13 Hidden Markov Models; Appendix: Mixture Software; References; Author Index; Subject Index
Record Nr. UNINA-9911020261603321
McLachlan Geoffrey J. <1946->  
New York, : Wiley, c2000
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui